Calculation update! New properties have been added to the website for dislocation monopole core structures, dynamic relaxes of both crystal and liquid phases, and melting temperatures! Currently, the results for these properties predominately focus on EAM-style potentials, but the results will be updated for other potentials as the associated calculations finish. Feel free to give us feedback on the new properties so we can improve their representations as needed.
Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
Citation: G. Sivaraman, J. Guo, L. Ward, N. Hoyt, M. Williamson, I. Foster, C. Benmore, and N. Jackson (2021), "Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl", The Journal of Physical Chemistry Letters12(17), 4278-4285. DOI: 10.1021/acs.jpclett.1c00901.
Abstract: The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.
Notes: This potential was designed for molten LiCl.